Overview

Modelling of road traffic noise and air pollution exposure for health studies requires detailed information on annual average daily traffic (AADT) flows on all roads. Europe-wide estimates on AADT are not publicly available, and thus, we aimed to fill this gap by building a model framework to estimate Europe-wide AADT. We collected open-source observations on AADT, and we built separate random forest (RF) models for different road types defined in OpenStreetMap. Predictors offered were population-, road-, and topology-related variables, collected from open-source data.

Here we publish the model framework with a subset of data (N=200) for test. The scripts for the model framework and validation can be found in src/.

Spatial maps of traffic estimates summed across various buffer sizes are freely available on Google Earth Engine (GEE) for academic use, as detailed in the Output section.

Project Structure

• read-only (RO): not edited by either code or researcher
• human-writeable (HW): edited by the researcher only.
• project-generated (PG): folders generated when running the code; these folders can be deleted or emptied and will be completely reconstituted as the project is run.

``` . ├── .gitignore ├── CITATION.cff ├── LICENSE ├── README.md ├── requirements.txt ├── data <- All project data, ignored by git │ ├── processed <- The final, canonical data sets for modeling. (PG) │ ├── workingData <- data generated during model development. (PG) │ ├── raw <- The original, immutable data dump. (RO) │ └── temp <- Intermediate data that has been transformed. (PG) └── src <- Source code for this project (HW)

```

Data data/

• ./data/raw/shared_dataN200.csv contains a subset of data points with observed AADT counts and derived predictor variables.
• Folder ./data/workingData/ contains all csv files generated during model development process.

source code src/

• 01_traffic_rf_5fold_finalv2_resNoXY.R builds random forests model with 5-fold CV setting.
• 02_evaluate_5foldCV.R evaluates the 5-fold CV result.
• fun_... are ancillary scripts.

Output

Spatial maps of traffic estimates in various buffer sizes (50m-5km)

We pubish spatial maps of the sum of the on-road AADT estimates within various circular buffer sizes (50 m, 100 m, 200 m, 300 m, 400 m, 500 m, 700 m, 1 km, 2 km, 5 km).

The spatial maps are stored as a multi-band image on Google Earth Engine (GEE), with each band labeled as aadt_[buffer in meters].

• Asset on GEE

• A GEE example code for visualization ```js var map = ee.Image("projects/ee-airview/assets/aadt"); var palette = ["000000", "#0000cd","#69a3cf","#7cb8de","#e2eb71", "#ebb671", "#e3702d", "#fa0000"]; Map.setCenter(7.5277, 51.754, 6) Map.addLayer(map.select("aadt_50"), {min:50000, max:500000, palette: palette}, "sum of AADT within 50 m")

Map.addLayer(map.select("aadt_500"), {min:0, max:10000000, palette: palette}, "sum of AADT within 500 m") ```

Citation

Shen, Y., de Hoogh, K., Schmitz, O., Gulliver, J., Vienneau, D., Vermeulen, R., Hoek, G., Karssenberg, D., 2024. Europe-wide high-spatial resolution air pollution models are improved by including traffic flow estimates on all roads. Atmos. Environ. 335, 120719. https://doi.org/10.1016/j.atmosenv.2024.120719

License

This project is licensed under the terms of the MIT License.